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access icon free Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene

Track-before-detect methods operate directly upon raw sensor signals without a separate, explicit detection stage. An efficient implementation of a Bayesian track-before-detect particle filter is described for tracking of a single target in a sequence of images. The filter produces a sample-based representation of the probability density function of the target state from raw pixel levels. An indication of the probability that the target is present is also provided. Spatial differentiation of the pixel array data allows objects to be tracked when viewed against a general scene with additive noise. Simulated results illustrate that a dim point target of unknown amplitude, which has become spatially blurred, may be tracked through a sequence of structured images. Detection sensitivity is established using simulated results. The novel aspect of the work is the efficient implementation – in particular, the calculation of the probability of the target being present.

References

    1. 1)
      • 5. Gustafsson, F.: ‘Statistical sensor fusion’ (Studentlitteratur, Lund, Sweden, 2012, Edition 2.1).
    2. 2)
      • 7. Rollason, M., Salmond, D.: ‘A particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene’. Proc. 5th IMA Conf. on Mathematics in Defence, Camberley, UK, November 2017.
    3. 3)
      • 12. Davey, S., Rutten, M., Cheung, B.: ‘A comparison of detection performance for several track-before-detect algorithms’. Int. Conf. on Information Fusion, Cologne, Germany, June 2008.
    4. 4)
      • 9. Gordon, N., Salmond, D., Smith, A.: ‘Novel approach to non-linear/non-Gaussian Bayesian state estimation problems’, IEE Proc. Radar, Sonar and Navigation, 1993, 140, (2), pp. 107113.
    5. 5)
      • 11. Ristic, B., Arulampalam, S., Gordon, N.: ‘Beyond the Kalman filter’ (Artech House, Boston, USA, 2004).
    6. 6)
      • 6. Li, C., Ji, H.: ‘Marginalized particle filter based track-before-detect algorithm for small dim infrared target’. Int. Conf. on Artificial Intelligence & Computational Intelligence, Shanghai, China, November 2009, pp. 321325.
    7. 7)
      • 3. Rollason, M., Salmond, D.: ‘A particle filter for track-before-detect of a target with unknown amplitude’. IEE Workshop Target Tracking: Algorithms and Applications, Enschede, Netherlands, 2001, pp. 14/114/4.
    8. 8)
      • 1. Salmond, D., Birch, H.: ‘A particle filter for track-before-detect’. Proc. American Control Conf., Arlington, USA, June 2001, pp. 37553760.
    9. 9)
      • 2. Maskell, S., Rollason, M., Salmond, D., et al: ‘Efficient particle filtering for multiple target tracking with application to tracking in structured images’. Proc. SPIE Signal & Data Processing of Small Targets, Orlando, USA, August 2002, pp. 215262.
    10. 10)
      • 4. Boers, Y., Driessen, J.: ‘Multitarget particle filter track before detect application’, IEE Proc. Radar, Sonar and Navigation, 2004, 151, (6), pp. 351357.
    11. 11)
      • 8. Lettington, A., Rollason, M., Tzimopoulou, S.: ‘Image restoration using a two-dimensional Lorentzian probability model’, J. Mod. Opt., 2000, 47, (5), pp. 931938.
    12. 12)
      • 10. Schon, T., Gustafsson, F., Nordlund, P.: ‘Marginalized particle filters for mixed linear/nonlinear state-space models’, IEEE Trans. Signal. Process., 2005, 53, (7), pp. 22792289.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2017.0483
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